Other methods which aim to instead re-label or re-weight datasets include [1]}, [2]}, [3]}, [4]}. Although many of these approaches are computationally efficient, they often do not consider elements in the domain which do not appear in the dataset and lack fairness guarantees. Similarly, repair methods aim to change the input data to break the dependence on sensitive attributes for trained classifiers [5]}, [6]}. To achieve distributional repair, one can employ frameworks of optimal transport [7]} and counterfactual distributions [8]}.
